store = {}
store['args']={'name': 'bald_mnist_703266', 'type': 'AcquisitionFunction.bald', 'seed': 703266, 'experiment_description': 'Coreset BALD vs BALD', 'acquisition_method': 'AcquisitionMethod.independent', 'available_sample_k': 1, 'num_inference_samples': 20, 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'target_accuracy': 0.96, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.mnist', 'initial_samples': [38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329], 'experiment_task_id': 9, 'experiments_laaos': './experiment_configs/coreset_bald_vs_bald/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=9', '--experiments_laaos=./experiment_configs/coreset_bald_vs_bald/configs.py']
store['iterations']=[]
store['initial_samples']=[38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6353, 'nll': 2.8089656269073484}, 'chosen_samples': ['19755'], 'chosen_samples_score': [1.284172649295673], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6629, 'nll': 2.3598756076812744}, 'chosen_samples': ['46897'], 'chosen_samples_score': [1.3039709105833786], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6959, 'nll': 2.0404547496795655}, 'chosen_samples': ['50908'], 'chosen_samples_score': [1.2696843217420075], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.708, 'nll': 1.8444227519989014}, 'chosen_samples': ['19315'], 'chosen_samples_score': [1.208456575855547], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6837, 'nll': 1.951486473083496}, 'chosen_samples': ['27294'], 'chosen_samples_score': [1.1512832128208623], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6825, 'nll': 1.9268299800872803}, 'chosen_samples': ['49784'], 'chosen_samples_score': [1.2151686169587932], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6897, 'nll': 1.8847576663970946}, 'chosen_samples': ['24437'], 'chosen_samples_score': [1.1663327753802124], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7172, 'nll': 1.675460546875}, 'chosen_samples': ['7207'], 'chosen_samples_score': [1.1398706159196075], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7328, 'nll': 1.6515122695922853}, 'chosen_samples': ['34560'], 'chosen_samples_score': [1.179206531158702], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7287, 'nll': 1.5530931348800658}, 'chosen_samples': ['16860'], 'chosen_samples_score': [1.1773076138676397], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.7506, 'nll': 1.687759422492981}, 'chosen_samples': ['46953'], 'chosen_samples_score': [1.2758456374392193], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7569, 'nll': 1.379319574356079}, 'chosen_samples': ['26380'], 'chosen_samples_score': [1.1283674874767189], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7426, 'nll': 1.6682413417816162}, 'chosen_samples': ['21049'], 'chosen_samples_score': [1.200729029336459], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7402, 'nll': 1.4326237718582153}, 'chosen_samples': ['14649'], 'chosen_samples_score': [1.1374542068078473], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7571, 'nll': 1.431545266532898}, 'chosen_samples': ['27652'], 'chosen_samples_score': [1.1082909950417354], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7835, 'nll': 1.2853941995620728}, 'chosen_samples': ['29410'], 'chosen_samples_score': [1.0860887412731257], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7636, 'nll': 1.2909635858535766}, 'chosen_samples': ['41468'], 'chosen_samples_score': [1.0111542385921388], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.772, 'nll': 1.3254488903045654}, 'chosen_samples': ['4846'], 'chosen_samples_score': [1.11552383088598], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7642, 'nll': 1.3002847085952758}, 'chosen_samples': ['23819'], 'chosen_samples_score': [1.048357337088974], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7646, 'nll': 1.3991605548858643}, 'chosen_samples': ['43238'], 'chosen_samples_score': [1.172422791204054], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7803, 'nll': 1.2159412761688233}, 'chosen_samples': ['8656'], 'chosen_samples_score': [1.0125349148048222], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7597, 'nll': 1.2913004388809204}, 'chosen_samples': ['44382'], 'chosen_samples_score': [1.104984241578086], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7633, 'nll': 1.3781545902252197}, 'chosen_samples': ['10997'], 'chosen_samples_score': [1.1005245712888767], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7791, 'nll': 1.249186547279358}, 'chosen_samples': ['15870'], 'chosen_samples_score': [1.0953735474983786], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7753, 'nll': 1.2871863748550414}, 'chosen_samples': ['21636'], 'chosen_samples_score': [1.003640458584187], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.798, 'nll': 1.1037786596298218}, 'chosen_samples': ['53848'], 'chosen_samples_score': [1.0513071625131019], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8243, 'nll': 1.1130708105087281}, 'chosen_samples': ['27323'], 'chosen_samples_score': [1.1939594963837075], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7747, 'nll': 1.2441878843307494}, 'chosen_samples': ['34122'], 'chosen_samples_score': [0.9763285196416676], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7971, 'nll': 1.1286769975662232}, 'chosen_samples': ['29904'], 'chosen_samples_score': [1.0469627610520056], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7935, 'nll': 1.0398361814498902}, 'chosen_samples': ['23305'], 'chosen_samples_score': [1.0599707989782352], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7918, 'nll': 1.1119427160263062}, 'chosen_samples': ['8122'], 'chosen_samples_score': [0.9852374488481164], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7829, 'nll': 1.1412040796279908}, 'chosen_samples': ['55481'], 'chosen_samples_score': [0.9171511110065088], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7819, 'nll': 1.1529315183639526}, 'chosen_samples': ['21451'], 'chosen_samples_score': [1.0739962774033267], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8051, 'nll': 1.0968988571166993}, 'chosen_samples': ['22813'], 'chosen_samples_score': [0.988411370142043], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8292, 'nll': 0.9676752639770507}, 'chosen_samples': ['50500'], 'chosen_samples_score': [0.9374148647473124], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.817, 'nll': 1.0116620597839356}, 'chosen_samples': ['21421'], 'chosen_samples_score': [1.034036877615881], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8209, 'nll': 1.0145903219223023}, 'chosen_samples': ['33593'], 'chosen_samples_score': [1.003523913551873], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8286, 'nll': 0.9464974374771118}, 'chosen_samples': ['1239'], 'chosen_samples_score': [1.0713730431760298], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8382, 'nll': 0.98471901512146}, 'chosen_samples': ['6755'], 'chosen_samples_score': [0.9779974216284859], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8229, 'nll': 0.9535341878890992}, 'chosen_samples': ['29032'], 'chosen_samples_score': [0.8725977965733012], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8246, 'nll': 1.066561452484131}, 'chosen_samples': ['56563'], 'chosen_samples_score': [1.1648126307063489], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7856, 'nll': 1.0767955003738403}, 'chosen_samples': ['47278'], 'chosen_samples_score': [0.8969855559056046], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8231, 'nll': 0.9184073829650878}, 'chosen_samples': ['59381'], 'chosen_samples_score': [0.8751254439283904], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8679, 'nll': 0.8937371013641358}, 'chosen_samples': ['28630'], 'chosen_samples_score': [1.225737477633186], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8391, 'nll': 0.8743600284576416}, 'chosen_samples': ['31396'], 'chosen_samples_score': [0.9352495370550968], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8287, 'nll': 0.8977742910385131}, 'chosen_samples': ['42687'], 'chosen_samples_score': [0.8857132279737333], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8271, 'nll': 0.8984023126602173}, 'chosen_samples': ['19959'], 'chosen_samples_score': [0.8703151165572547], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8622, 'nll': 0.8832488147735595}, 'chosen_samples': ['48789'], 'chosen_samples_score': [1.064935609667136], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8795, 'nll': 0.8095953201293945}, 'chosen_samples': ['12181'], 'chosen_samples_score': [1.0546284051368713], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8776, 'nll': 0.7898299132347107}, 'chosen_samples': ['5728'], 'chosen_samples_score': [1.0850992575959055], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8747, 'nll': 0.7328936819076538}, 'chosen_samples': ['57463'], 'chosen_samples_score': [0.8495968165864269], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8918, 'nll': 0.7642073696136474}, 'chosen_samples': ['3972'], 'chosen_samples_score': [1.1212795429903815], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8772, 'nll': 0.7748915761947632}, 'chosen_samples': ['57507'], 'chosen_samples_score': [1.0909924782292437], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8541, 'nll': 0.860238045501709}, 'chosen_samples': ['4315'], 'chosen_samples_score': [1.004076268579493], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8624, 'nll': 0.839123885345459}, 'chosen_samples': ['16084'], 'chosen_samples_score': [1.0670765581456412], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.876, 'nll': 0.7805511392593384}, 'chosen_samples': ['11581'], 'chosen_samples_score': [1.0586361987181185], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8911, 'nll': 0.7732703149795532}, 'chosen_samples': ['30688'], 'chosen_samples_score': [1.135811658393199], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8855, 'nll': 0.7361440223693848}, 'chosen_samples': ['27317'], 'chosen_samples_score': [1.0765823025474504], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8844, 'nll': 0.7407243851661682}, 'chosen_samples': ['51304'], 'chosen_samples_score': [1.0999769245622948], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8794, 'nll': 0.7682739971160889}, 'chosen_samples': ['2381'], 'chosen_samples_score': [1.071192264642142], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8863, 'nll': 0.7232827204704285}, 'chosen_samples': ['13709'], 'chosen_samples_score': [1.1068483901709738], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8837, 'nll': 0.722884804725647}, 'chosen_samples': ['161'], 'chosen_samples_score': [1.0429604400517882], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8901, 'nll': 0.7187669358253479}, 'chosen_samples': ['27209'], 'chosen_samples_score': [1.112253425346116], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8897, 'nll': 0.7491636063575745}, 'chosen_samples': ['47597'], 'chosen_samples_score': [1.1588041174142745], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8844, 'nll': 0.7312252689361572}, 'chosen_samples': ['35246'], 'chosen_samples_score': [1.1619720586101323], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8868, 'nll': 0.6898056845664978}, 'chosen_samples': ['36450'], 'chosen_samples_score': [1.0310781389815389], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8839, 'nll': 0.7086520466804505}, 'chosen_samples': ['45047'], 'chosen_samples_score': [1.0490868043210169], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8975, 'nll': 0.6854281355857849}, 'chosen_samples': ['38409'], 'chosen_samples_score': [1.1066186120623211], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8792, 'nll': 0.7320096118927002}, 'chosen_samples': ['45917'], 'chosen_samples_score': [0.9808176856599635], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8965, 'nll': 0.6945108024597167}, 'chosen_samples': ['40766'], 'chosen_samples_score': [1.1456062747521378], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8957, 'nll': 0.6370636122703552}, 'chosen_samples': ['54534'], 'chosen_samples_score': [0.9517816387858061], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8908, 'nll': 0.7190648698806763}, 'chosen_samples': ['23435'], 'chosen_samples_score': [1.0747966812969585], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.897, 'nll': 0.6577127873420715}, 'chosen_samples': ['46368'], 'chosen_samples_score': [0.9816865400991575], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8921, 'nll': 0.6651569620132446}, 'chosen_samples': ['54954'], 'chosen_samples_score': [1.057226035797982], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9034, 'nll': 0.6216500154495239}, 'chosen_samples': ['670'], 'chosen_samples_score': [1.0516961249614556], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8989, 'nll': 0.6339713474273682}, 'chosen_samples': ['56480'], 'chosen_samples_score': [0.9666367456042957], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8956, 'nll': 0.6681652764320374}, 'chosen_samples': ['6428'], 'chosen_samples_score': [1.0344335950902106], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9, 'nll': 0.6696741127967835}, 'chosen_samples': ['37048'], 'chosen_samples_score': [0.9966299574394767], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9032, 'nll': 0.6352040150642395}, 'chosen_samples': ['8715'], 'chosen_samples_score': [1.0342129922129493], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8945, 'nll': 0.6658528683662415}, 'chosen_samples': ['4955'], 'chosen_samples_score': [1.041476892368157], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8907, 'nll': 0.7473341143608093}, 'chosen_samples': ['43817'], 'chosen_samples_score': [1.122708992755049], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8926, 'nll': 0.6788747166633606}, 'chosen_samples': ['37247'], 'chosen_samples_score': [1.1033110956621839], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8867, 'nll': 0.6967244803428649}, 'chosen_samples': ['28215'], 'chosen_samples_score': [1.0041243229575945], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9101, 'nll': 0.6430035607337952}, 'chosen_samples': ['14866'], 'chosen_samples_score': [1.166526681810174], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8902, 'nll': 0.6495760633468628}, 'chosen_samples': ['25919'], 'chosen_samples_score': [0.9576976334015705], 'chosen_samples_orignal_score': None})
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